Control of particle agglomeration

with relevance to after-treatment

gas processes

Ghulam Mustafa Majal

Content

• Introduction

• Short term goals

• Preliminary study

• Future plans

Motivation

• Particulate emission from internal combustion

engines(ICE) are a major health issue.

• Such emission contain fine particles with a size distribution

around the order of nano sized particles.

• These small particles are able to penetrate through the

human respiratory system and potentially cause health

problems.

• Larger particles on the other hand are easier to filtrate and

are less likely to contribute to respiratory disorders.

• Particle agglomeration is one way in which larger particles

can be obtained from ICE particulate emissions.

Findings of the EEA report No 5/2014

• According to a report published by the European

Environmental agency in 2014, particulate matter can

have adverse affects not only on humans but also on

animals and plants. They can also damage buildings.

• An ETC/ACM systematic estimate of the health impacts

attributable to exposure to particulate matter(PM) was

reported. It showed that the effect of 𝑃𝑀2.5 concentrations

on total mortality lead to about 458 000 premature deaths

per year in Europe. The estimate was based on the

concentration levels found in 2011.

Scope of the project

• One of the main goals of the project is to find methods for

enabling agglomeration of particles, based on

manipulation of the hydrodynamic and acoustic fields, in

the ICE flow exhaust system.

• This project will focus on building the theoretical

framework concerning particle motion and grouping in

oscillatory flows and acoustic fields using computational

tools.

• Suitable models will be developed after a basic

understanding of the physical phenomena involved is

reached. These models will later be used to simulate a

real engine exhaust system.

Short term goals

• Simulate simplified cases of single & two-phase flows

using 1D models.

• Identify benchmark cases to be used for model verification

and validation.

• Perform sensitivity studies in order to investigate the

effects of different parameters on "Inertia"

grouping/agglomeration.

• Examine the possibility of incorporating acoustic forcing

within the modeling framework.

• Look towards more complex models utilized to simulate

2D and 3D geometry.

• Identify benchmark cases for software verification.

Preliminary study

• Initial literature review has been performed on the papers

published by David Katoshevski.

• Numerical experiments have been performed in order to

reproduce results presented in his papers.

• A simple 1D model is used in order to study the process of

grouping of particles in an oscillating flow field.

Description of the simplified 1D model

• The model considers an oscillating Stokesian flow in the

frame of reference moving with the phase velocity of the

wave.

• In order to simplify the analysis a few assumptions have

been made.

• The effects of particles on the host gas have been

ignored.

• The equation governing the dynamics of the particle only

take into account viscous forces.

Mathematical representation

The velocity of the flow field at position 𝑥 at time 𝑡 is given by

𝑣𝑔 𝑥, 𝑡 = 𝑉𝑎 − 𝑉𝑏 sin(𝑘𝑥 − 𝜔𝑡),

where 𝑉𝑎 is the dimensional mean flow velocity, 𝑉𝑏 is the

dimensional amplitude of the oscillations in the 𝑥-direction, 𝑘 is

the wave number and 𝜔 is the angular velocity.

The equation governing the dynamics of the particles inside this

flow field can be expressed as:

𝑥 =1

𝜏𝑝𝑣𝑔 − 𝑥 ,

where 𝜏𝑝 =𝜌𝑝𝐷𝑝

2

18𝜇, with 𝜌𝑝 being the particle density, 𝐷𝑝 being the

particle diameter and 𝜇 being the dynamic viscosity of the gas.

Results from theoretical analysis

• For this particular model it was observed that a

parameter 𝛽 played a crucial role in identifying

cases where grouping occurs.

• The parameter is defined as 𝛽 =𝑈𝑎−1

𝑈𝑏, where 𝑈𝑎

and 𝑈𝑏 are non dimensional versions of 𝑉𝑎 and 𝑉𝑏.

• Three cases were analyzed: 𝛽 < 1, 𝛽 = 1 and 𝛽 > 1.

• It was observed that grouping occurs for the first two

cases but not for the last case.

Results produced for the case of 𝛽 < 𝟏

Results produced for the case of 𝛽 = 𝟏

Future plans

• Understand the limitations of this model in order to see the

extent of its applicability.

• Identify more complex models and approaches for

simulating particles inside an oscillating flow field with or

without external forcing.

• Carry out further experiments in order to examine the

significance of other parameters with regards to

agglomeration.

• Look into the possibility of incorporating acoustic forcing.

References

1. S.Sazhin et al., Particle grouping in oscillating flows.

European Journal of Mechanical B/Fluids(2007).

2. D.Katoshevski et al., Grouping and trapping of

evaporating droplets in an oscillating flow. International

Journal of Heat and Fluid Flow 29(2008) 415-426.

3. D.Katoshevski et al., Aerosol Clustering in oscillating

flows: Mathematical Analysis. Atomization and Sprays,

vol. 15 , 2005.

4. D.Katoshevski et al., Particle grouping, a new method for

reducing emission of submicron particles from diesel

engines. Fuel 89 (2010) 2411–2416.